{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering on NCAA data\n",
    "\n",
    "Domain knowledge is critical to getting the best out of data analysis and machine learning.\n",
    "In the case of basketball, Dean Oliver identified four factors that are critical to success:\n",
    "* Shooting\n",
    "* Turnovers\n",
    "* Rebounding\n",
    "* Free Throws\n",
    "\n",
    "Of course, it is not enough to identify factors, you need a way to measure them.\n",
    "\n",
    "Read [this article](https://www.basketball-reference.com/about/factors.html) about the four factors and how they are measured. In this notebook, we will compute them from the box score data. The numbers are slightly different from that of the article because the article is about the NBA, but these numbers are Dean Oliver's variants for NCAA games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shooting efficiency\n",
    "\n",
    "Shooting is measured as the fraction of field goal attempts made, weighting 3 points higher:\n",
    "\n",
    "$(FG + 0.5 * 3P) / FGA$\n",
    "\n",
    "Let's compute the offensive and defensive shooting efficiency and see how correlated they are to winning teams.\n",
    "\n",
    "See [%%bigquery documentation](https://googleapis.github.io/google-cloud-python/latest/bigquery/magics.html) for how to use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_code</th>\n",
       "      <th>offensive_shooting_efficiency</th>\n",
       "      <th>opponents_shooting_efficiency</th>\n",
       "      <th>win_rate</th>\n",
       "      <th>num_games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>272</td>\n",
       "      <td>0.506978</td>\n",
       "      <td>0.516485</td>\n",
       "      <td>0.426626</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>632</td>\n",
       "      <td>0.473055</td>\n",
       "      <td>0.504116</td>\n",
       "      <td>0.429664</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178</td>\n",
       "      <td>0.469923</td>\n",
       "      <td>0.512281</td>\n",
       "      <td>0.320840</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>504980</td>\n",
       "      <td>0.461662</td>\n",
       "      <td>0.567646</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>183</td>\n",
       "      <td>0.544678</td>\n",
       "      <td>0.519526</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28</td>\n",
       "      <td>0.516613</td>\n",
       "      <td>0.503602</td>\n",
       "      <td>0.548433</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>483</td>\n",
       "      <td>0.504365</td>\n",
       "      <td>0.513276</td>\n",
       "      <td>0.443769</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.461051</td>\n",
       "      <td>0.500835</td>\n",
       "      <td>0.413445</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>664</td>\n",
       "      <td>0.500198</td>\n",
       "      <td>0.509669</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>204</td>\n",
       "      <td>0.483567</td>\n",
       "      <td>0.473647</td>\n",
       "      <td>0.530864</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>229</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>0.487914</td>\n",
       "      <td>0.382309</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>158</td>\n",
       "      <td>0.513192</td>\n",
       "      <td>0.503893</td>\n",
       "      <td>0.467949</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>136</td>\n",
       "      <td>0.444162</td>\n",
       "      <td>0.542146</td>\n",
       "      <td>0.202683</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>141</td>\n",
       "      <td>0.506201</td>\n",
       "      <td>0.545918</td>\n",
       "      <td>0.337719</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>402</td>\n",
       "      <td>0.481683</td>\n",
       "      <td>0.508385</td>\n",
       "      <td>0.385321</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>676</td>\n",
       "      <td>0.527071</td>\n",
       "      <td>0.493973</td>\n",
       "      <td>0.748201</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>172</td>\n",
       "      <td>0.496961</td>\n",
       "      <td>0.518819</td>\n",
       "      <td>0.343154</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2678</td>\n",
       "      <td>0.467513</td>\n",
       "      <td>0.508301</td>\n",
       "      <td>0.345312</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>231</td>\n",
       "      <td>0.495587</td>\n",
       "      <td>0.501348</td>\n",
       "      <td>0.417630</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>699</td>\n",
       "      <td>0.499126</td>\n",
       "      <td>0.502554</td>\n",
       "      <td>0.517974</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>471</td>\n",
       "      <td>0.506805</td>\n",
       "      <td>0.493353</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>554</td>\n",
       "      <td>0.532023</td>\n",
       "      <td>0.486887</td>\n",
       "      <td>0.646865</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>288</td>\n",
       "      <td>0.512249</td>\n",
       "      <td>0.475227</td>\n",
       "      <td>0.633001</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>101</td>\n",
       "      <td>0.489015</td>\n",
       "      <td>0.517949</td>\n",
       "      <td>0.340989</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1334</td>\n",
       "      <td>0.430077</td>\n",
       "      <td>0.557402</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>572</td>\n",
       "      <td>0.493080</td>\n",
       "      <td>0.482184</td>\n",
       "      <td>0.572183</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>742</td>\n",
       "      <td>0.524123</td>\n",
       "      <td>0.491863</td>\n",
       "      <td>0.552743</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>782</td>\n",
       "      <td>0.519189</td>\n",
       "      <td>0.464462</td>\n",
       "      <td>0.779202</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>721</td>\n",
       "      <td>0.513223</td>\n",
       "      <td>0.523255</td>\n",
       "      <td>0.417981</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>697</td>\n",
       "      <td>0.501382</td>\n",
       "      <td>0.474652</td>\n",
       "      <td>0.580210</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>30036</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>0.690715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>199</td>\n",
       "      <td>0.417882</td>\n",
       "      <td>0.612814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>30232</td>\n",
       "      <td>0.344412</td>\n",
       "      <td>0.632807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>8911</td>\n",
       "      <td>0.430807</td>\n",
       "      <td>0.608784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>119</td>\n",
       "      <td>0.443858</td>\n",
       "      <td>0.578475</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>506514</td>\n",
       "      <td>0.313492</td>\n",
       "      <td>0.606239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>1079</td>\n",
       "      <td>0.424555</td>\n",
       "      <td>0.525222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>20</td>\n",
       "      <td>0.400667</td>\n",
       "      <td>0.480387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>24400</td>\n",
       "      <td>0.435043</td>\n",
       "      <td>0.761804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>30209</td>\n",
       "      <td>0.581699</td>\n",
       "      <td>0.611641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>30222</td>\n",
       "      <td>0.420286</td>\n",
       "      <td>0.626385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>506332</td>\n",
       "      <td>0.399307</td>\n",
       "      <td>0.433187</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>18422</td>\n",
       "      <td>0.376947</td>\n",
       "      <td>0.698700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>503863</td>\n",
       "      <td>0.372083</td>\n",
       "      <td>0.679280</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>506495</td>\n",
       "      <td>0.365774</td>\n",
       "      <td>0.630245</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>30165</td>\n",
       "      <td>0.256732</td>\n",
       "      <td>0.514999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>506534</td>\n",
       "      <td>0.403608</td>\n",
       "      <td>0.472195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>803</td>\n",
       "      <td>0.301586</td>\n",
       "      <td>0.640208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>16142</td>\n",
       "      <td>0.403827</td>\n",
       "      <td>0.681255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>9684</td>\n",
       "      <td>0.353597</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>506190</td>\n",
       "      <td>0.469890</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1155</th>\n",
       "      <td>506063</td>\n",
       "      <td>0.453915</td>\n",
       "      <td>0.507163</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>681</td>\n",
       "      <td>0.395522</td>\n",
       "      <td>0.669123</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1157</th>\n",
       "      <td>506469</td>\n",
       "      <td>0.409970</td>\n",
       "      <td>0.614945</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>506232</td>\n",
       "      <td>0.318842</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>504906</td>\n",
       "      <td>0.418800</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>507</td>\n",
       "      <td>0.558824</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>2814</td>\n",
       "      <td>0.491995</td>\n",
       "      <td>0.428956</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>500615</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.378571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1163</th>\n",
       "      <td>9013</td>\n",
       "      <td>0.453704</td>\n",
       "      <td>0.306818</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1164 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      team_code  offensive_shooting_efficiency  opponents_shooting_efficiency  \\\n",
       "0           272                       0.506978                       0.516485   \n",
       "1           632                       0.473055                       0.504116   \n",
       "2           178                       0.469923                       0.512281   \n",
       "3        504980                       0.461662                       0.567646   \n",
       "4           183                       0.544678                       0.519526   \n",
       "5            28                       0.516613                       0.503602   \n",
       "6           483                       0.504365                       0.513276   \n",
       "7             7                       0.461051                       0.500835   \n",
       "8           664                       0.500198                       0.509669   \n",
       "9           204                       0.483567                       0.473647   \n",
       "10          229                       0.479087                       0.487914   \n",
       "11          158                       0.513192                       0.503893   \n",
       "12          136                       0.444162                       0.542146   \n",
       "13          141                       0.506201                       0.545918   \n",
       "14          402                       0.481683                       0.508385   \n",
       "15          676                       0.527071                       0.493973   \n",
       "16          172                       0.496961                       0.518819   \n",
       "17         2678                       0.467513                       0.508301   \n",
       "18          231                       0.495587                       0.501348   \n",
       "19          699                       0.499126                       0.502554   \n",
       "20          471                       0.506805                       0.493353   \n",
       "21          554                       0.532023                       0.486887   \n",
       "22          288                       0.512249                       0.475227   \n",
       "23          101                       0.489015                       0.517949   \n",
       "24         1334                       0.430077                       0.557402   \n",
       "25          572                       0.493080                       0.482184   \n",
       "26          742                       0.524123                       0.491863   \n",
       "27          782                       0.519189                       0.464462   \n",
       "28          721                       0.513223                       0.523255   \n",
       "29          697                       0.501382                       0.474652   \n",
       "...         ...                            ...                            ...   \n",
       "1134      30036                       0.406250                       0.690715   \n",
       "1135        199                       0.417882                       0.612814   \n",
       "1136      30232                       0.344412                       0.632807   \n",
       "1137       8911                       0.430807                       0.608784   \n",
       "1138        119                       0.443858                       0.578475   \n",
       "1139     506514                       0.313492                       0.606239   \n",
       "1140       1079                       0.424555                       0.525222   \n",
       "1141         20                       0.400667                       0.480387   \n",
       "1142      24400                       0.435043                       0.761804   \n",
       "1143      30209                       0.581699                       0.611641   \n",
       "1144      30222                       0.420286                       0.626385   \n",
       "1145     506332                       0.399307                       0.433187   \n",
       "1146      18422                       0.376947                       0.698700   \n",
       "1147     503863                       0.372083                       0.679280   \n",
       "1148     506495                       0.365774                       0.630245   \n",
       "1149      30165                       0.256732                       0.514999   \n",
       "1150     506534                       0.403608                       0.472195   \n",
       "1151        803                       0.301586                       0.640208   \n",
       "1152      16142                       0.403827                       0.681255   \n",
       "1153       9684                       0.353597                       0.535714   \n",
       "1154     506190                       0.469890                       0.771429   \n",
       "1155     506063                       0.453915                       0.507163   \n",
       "1156        681                       0.395522                       0.669123   \n",
       "1157     506469                       0.409970                       0.614945   \n",
       "1158     506232                       0.318842                       0.615385   \n",
       "1159     504906                       0.418800                       0.576923   \n",
       "1160        507                       0.558824                       0.384615   \n",
       "1161       2814                       0.491995                       0.428956   \n",
       "1162     500615                       0.500000                       0.378571   \n",
       "1163       9013                       0.453704                       0.306818   \n",
       "\n",
       "      win_rate  num_games  \n",
       "0     0.426626        661  \n",
       "1     0.429664        654  \n",
       "2     0.320840        667  \n",
       "3     0.000000         25  \n",
       "4     0.476190        504  \n",
       "5     0.548433        702  \n",
       "6     0.443769        658  \n",
       "7     0.413445        595  \n",
       "8     0.520000        675  \n",
       "9     0.530864        729  \n",
       "10    0.382309        667  \n",
       "11    0.467949        624  \n",
       "12    0.202683        671  \n",
       "13    0.337719        684  \n",
       "14    0.385321        654  \n",
       "15    0.748201        695  \n",
       "16    0.343154        577  \n",
       "17    0.345312        640  \n",
       "18    0.417630        692  \n",
       "19    0.517974        612  \n",
       "20    0.513514        629  \n",
       "21    0.646865        606  \n",
       "22    0.633001        703  \n",
       "23    0.340989        566  \n",
       "24    0.069767         43  \n",
       "25    0.572183        568  \n",
       "26    0.552743        711  \n",
       "27    0.779202        702  \n",
       "28    0.417981        634  \n",
       "29    0.580210        667  \n",
       "...        ...        ...  \n",
       "1134  0.000000          3  \n",
       "1135  0.000000          6  \n",
       "1136  0.000000          6  \n",
       "1137  0.000000          6  \n",
       "1138  0.000000          3  \n",
       "1139  0.000000          9  \n",
       "1140  0.000000          3  \n",
       "1141  0.000000          3  \n",
       "1142  0.000000          3  \n",
       "1143  0.000000          3  \n",
       "1144  0.000000          3  \n",
       "1145  0.000000          3  \n",
       "1146  0.000000          3  \n",
       "1147  0.000000          3  \n",
       "1148  0.000000          3  \n",
       "1149  0.000000          3  \n",
       "1150  0.000000          3  \n",
       "1151  0.000000          3  \n",
       "1152  0.000000          3  \n",
       "1153  0.000000          3  \n",
       "1154  0.000000          3  \n",
       "1155  0.000000          3  \n",
       "1156  0.000000          3  \n",
       "1157  0.000000          3  \n",
       "1158  0.000000          3  \n",
       "1159  0.000000          3  \n",
       "1160  1.000000          1  \n",
       "1161  1.000000          2  \n",
       "1162  1.000000          1  \n",
       "1163  1.000000          1  \n",
       "\n",
       "[1164 rows x 5 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery df1\n",
    "SELECT \n",
    "  team_code,\n",
    "  AVG(SAFE_DIVIDE(fgm + 0.5 * fgm3,fga)) AS offensive_shooting_efficiency,\n",
    "  AVG(SAFE_DIVIDE(opp_fgm + 0.5 * opp_fgm3,opp_fga)) AS opponents_shooting_efficiency,\n",
    "  AVG(win) AS win_rate,\n",
    "  COUNT(win) AS num_games\n",
    "FROM lab_dev.team_box\n",
    "WHERE fga IS NOT NULL\n",
    "GROUP BY team_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the entries corresponding to teams that played fewer than 100 games, and then plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1[df1['num_games'] > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(x='offensive_shooting_efficiency', y='win_rate', style='o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(x='opponents_shooting_efficiency', y='win_rate', style='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the relationship make sense? Do you think offensive and defensive efficiency are good predictors of a team's performance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turnover Percentage\n",
    "\n",
    "Turnover percentage is measured as:\n",
    "\n",
    "$TOV / (FGA + 0.475 * FTA + TOV - OREB)$\n",
    "\n",
    "As before, let's compute this, and see whether it is a good predictor. For simplicity, we will compute only offensive turnover percentage, although we should really compute both sides as we did for scoring efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_code</th>\n",
       "      <th>turnover_percent</th>\n",
       "      <th>win_rate</th>\n",
       "      <th>num_games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>272</td>\n",
       "      <td>0.191072</td>\n",
       "      <td>0.426626</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>632</td>\n",
       "      <td>0.212263</td>\n",
       "      <td>0.429664</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178</td>\n",
       "      <td>0.197997</td>\n",
       "      <td>0.320840</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>183</td>\n",
       "      <td>0.196554</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>0.174868</td>\n",
       "      <td>0.548433</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>483</td>\n",
       "      <td>0.202556</td>\n",
       "      <td>0.443769</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.202094</td>\n",
       "      <td>0.413445</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>664</td>\n",
       "      <td>0.183439</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>204</td>\n",
       "      <td>0.185664</td>\n",
       "      <td>0.530864</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>229</td>\n",
       "      <td>0.186421</td>\n",
       "      <td>0.382309</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>158</td>\n",
       "      <td>0.182368</td>\n",
       "      <td>0.467949</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>136</td>\n",
       "      <td>0.205889</td>\n",
       "      <td>0.202683</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>141</td>\n",
       "      <td>0.185401</td>\n",
       "      <td>0.337719</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>402</td>\n",
       "      <td>0.183679</td>\n",
       "      <td>0.385321</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>676</td>\n",
       "      <td>0.204146</td>\n",
       "      <td>0.748201</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>172</td>\n",
       "      <td>0.199702</td>\n",
       "      <td>0.343154</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2678</td>\n",
       "      <td>0.229683</td>\n",
       "      <td>0.345312</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>231</td>\n",
       "      <td>0.194933</td>\n",
       "      <td>0.417630</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>699</td>\n",
       "      <td>0.192619</td>\n",
       "      <td>0.517974</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>471</td>\n",
       "      <td>0.190303</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>554</td>\n",
       "      <td>0.174488</td>\n",
       "      <td>0.646865</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>288</td>\n",
       "      <td>0.175029</td>\n",
       "      <td>0.633001</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>101</td>\n",
       "      <td>0.188541</td>\n",
       "      <td>0.340989</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>572</td>\n",
       "      <td>0.182393</td>\n",
       "      <td>0.572183</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>742</td>\n",
       "      <td>0.176804</td>\n",
       "      <td>0.552743</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>782</td>\n",
       "      <td>0.166937</td>\n",
       "      <td>0.779202</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>721</td>\n",
       "      <td>0.190736</td>\n",
       "      <td>0.417981</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>697</td>\n",
       "      <td>0.192820</td>\n",
       "      <td>0.580210</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>725</td>\n",
       "      <td>0.190195</td>\n",
       "      <td>0.465774</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>386</td>\n",
       "      <td>0.196228</td>\n",
       "      <td>0.285491</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>716</td>\n",
       "      <td>0.183046</td>\n",
       "      <td>0.424818</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>328</td>\n",
       "      <td>0.181668</td>\n",
       "      <td>0.813538</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>488</td>\n",
       "      <td>0.209874</td>\n",
       "      <td>0.356932</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>738</td>\n",
       "      <td>0.184885</td>\n",
       "      <td>0.704802</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>711</td>\n",
       "      <td>0.190054</td>\n",
       "      <td>0.492997</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>514</td>\n",
       "      <td>0.173974</td>\n",
       "      <td>0.564560</td>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>392</td>\n",
       "      <td>0.190724</td>\n",
       "      <td>0.680224</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>305</td>\n",
       "      <td>0.187948</td>\n",
       "      <td>0.502146</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>260</td>\n",
       "      <td>0.166279</td>\n",
       "      <td>0.859211</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>553</td>\n",
       "      <td>0.202890</td>\n",
       "      <td>0.385399</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>371</td>\n",
       "      <td>0.195883</td>\n",
       "      <td>0.562412</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>28600</td>\n",
       "      <td>0.193170</td>\n",
       "      <td>0.556492</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>472</td>\n",
       "      <td>0.194620</td>\n",
       "      <td>0.731973</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>457</td>\n",
       "      <td>0.169430</td>\n",
       "      <td>0.750968</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>355</td>\n",
       "      <td>0.195786</td>\n",
       "      <td>0.501408</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>416</td>\n",
       "      <td>0.187034</td>\n",
       "      <td>0.738622</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>17</td>\n",
       "      <td>0.205061</td>\n",
       "      <td>0.355200</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>556</td>\n",
       "      <td>0.175228</td>\n",
       "      <td>0.593496</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>80</td>\n",
       "      <td>0.194060</td>\n",
       "      <td>0.397380</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>14927</td>\n",
       "      <td>0.186778</td>\n",
       "      <td>0.711409</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>741</td>\n",
       "      <td>0.180576</td>\n",
       "      <td>0.345576</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>650</td>\n",
       "      <td>0.168030</td>\n",
       "      <td>0.501916</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>334</td>\n",
       "      <td>0.171997</td>\n",
       "      <td>0.810710</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>688</td>\n",
       "      <td>0.177524</td>\n",
       "      <td>0.681392</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1104</td>\n",
       "      <td>0.177548</td>\n",
       "      <td>0.647287</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>464</td>\n",
       "      <td>0.170675</td>\n",
       "      <td>0.481720</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>505</td>\n",
       "      <td>0.181377</td>\n",
       "      <td>0.538062</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2</td>\n",
       "      <td>0.197957</td>\n",
       "      <td>0.462247</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>368</td>\n",
       "      <td>0.200837</td>\n",
       "      <td>0.409836</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2743</td>\n",
       "      <td>0.193202</td>\n",
       "      <td>0.483301</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team_code  turnover_percent  win_rate  num_games\n",
       "0          272          0.191072  0.426626        661\n",
       "1          632          0.212263  0.429664        654\n",
       "2          178          0.197997  0.320840        667\n",
       "3          183          0.196554  0.476190        504\n",
       "4           28          0.174868  0.548433        702\n",
       "5          483          0.202556  0.443769        658\n",
       "6            7          0.202094  0.413445        595\n",
       "7          664          0.183439  0.520000        675\n",
       "8          204          0.185664  0.530864        729\n",
       "9          229          0.186421  0.382309        667\n",
       "10         158          0.182368  0.467949        624\n",
       "11         136          0.205889  0.202683        671\n",
       "12         141          0.185401  0.337719        684\n",
       "13         402          0.183679  0.385321        654\n",
       "14         676          0.204146  0.748201        695\n",
       "15         172          0.199702  0.343154        577\n",
       "16        2678          0.229683  0.345312        640\n",
       "17         231          0.194933  0.417630        692\n",
       "18         699          0.192619  0.517974        612\n",
       "19         471          0.190303  0.513514        629\n",
       "20         554          0.174488  0.646865        606\n",
       "21         288          0.175029  0.633001        703\n",
       "22         101          0.188541  0.340989        566\n",
       "23         572          0.182393  0.572183        568\n",
       "24         742          0.176804  0.552743        711\n",
       "25         782          0.166937  0.779202        702\n",
       "26         721          0.190736  0.417981        634\n",
       "27         697          0.192820  0.580210        667\n",
       "28         725          0.190195  0.465774        672\n",
       "29         386          0.196228  0.285491        641\n",
       "..         ...               ...       ...        ...\n",
       "321        716          0.183046  0.424818        685\n",
       "322        328          0.181668  0.813538        783\n",
       "323        488          0.209874  0.356932        678\n",
       "324        738          0.184885  0.704802        708\n",
       "325        711          0.190054  0.492997        714\n",
       "326        514          0.173974  0.564560        728\n",
       "327        392          0.190724  0.680224        713\n",
       "328        305          0.187948  0.502146        699\n",
       "329        260          0.166279  0.859211        760\n",
       "330        553          0.202890  0.385399        589\n",
       "331        371          0.195883  0.562412        713\n",
       "332      28600          0.193170  0.556492        593\n",
       "333        472          0.194620  0.731973        735\n",
       "334        457          0.169430  0.750968        775\n",
       "335        355          0.195786  0.501408        710\n",
       "336        416          0.187034  0.738622        769\n",
       "337         17          0.205061  0.355200        625\n",
       "338        556          0.175228  0.593496        738\n",
       "339         80          0.194060  0.397380        458\n",
       "340      14927          0.186778  0.711409        596\n",
       "341        741          0.180576  0.345576        599\n",
       "342        650          0.168030  0.501916        522\n",
       "343        334          0.171997  0.810710        803\n",
       "344        688          0.177524  0.681392        747\n",
       "345       1104          0.177548  0.647287        516\n",
       "346        464          0.170675  0.481720        465\n",
       "347        505          0.181377  0.538062        578\n",
       "348          2          0.197957  0.462247        543\n",
       "349        368          0.200837  0.409836        488\n",
       "350       2743          0.193202  0.483301        509\n",
       "\n",
       "[351 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery df2\n",
    "SELECT \n",
    "  team_code,\n",
    "  AVG(SAFE_DIVIDE(tov,fga+0.475*fta+tov-oreb)) AS turnover_percent,\n",
    "  AVG(win) AS win_rate,\n",
    "  COUNT(win) AS num_games\n",
    "FROM lab_dev.team_box\n",
    "WHERE fga IS NOT NULL\n",
    "GROUP BY team_code\n",
    "HAVING num_games > 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(x='turnover_percent', y='win_rate', style='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rebounding\n",
    "\n",
    "Again, we'd have to measure both sides, but for simplicity, we'll do only the offensive rebounds.\n",
    "\n",
    "$ORB / (ORB + Opp DRB)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_code</th>\n",
       "      <th>rebounding</th>\n",
       "      <th>win_rate</th>\n",
       "      <th>num_games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>272</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.426626</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>632</td>\n",
       "      <td>0.293107</td>\n",
       "      <td>0.429664</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178</td>\n",
       "      <td>0.297945</td>\n",
       "      <td>0.320840</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>183</td>\n",
       "      <td>0.245612</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>0.283190</td>\n",
       "      <td>0.548433</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>483</td>\n",
       "      <td>0.276217</td>\n",
       "      <td>0.443769</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.336807</td>\n",
       "      <td>0.413445</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>664</td>\n",
       "      <td>0.287098</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>204</td>\n",
       "      <td>0.322037</td>\n",
       "      <td>0.530864</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>229</td>\n",
       "      <td>0.291606</td>\n",
       "      <td>0.382309</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>158</td>\n",
       "      <td>0.289716</td>\n",
       "      <td>0.467949</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>136</td>\n",
       "      <td>0.282258</td>\n",
       "      <td>0.202683</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>141</td>\n",
       "      <td>0.283392</td>\n",
       "      <td>0.337719</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>402</td>\n",
       "      <td>0.314090</td>\n",
       "      <td>0.385321</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>676</td>\n",
       "      <td>0.352438</td>\n",
       "      <td>0.748201</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>172</td>\n",
       "      <td>0.287586</td>\n",
       "      <td>0.343154</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2678</td>\n",
       "      <td>0.282616</td>\n",
       "      <td>0.345312</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>231</td>\n",
       "      <td>0.295516</td>\n",
       "      <td>0.417630</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>699</td>\n",
       "      <td>0.315440</td>\n",
       "      <td>0.517974</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>471</td>\n",
       "      <td>0.287515</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>554</td>\n",
       "      <td>0.255758</td>\n",
       "      <td>0.646865</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>288</td>\n",
       "      <td>0.331052</td>\n",
       "      <td>0.633001</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>101</td>\n",
       "      <td>0.297901</td>\n",
       "      <td>0.340989</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>572</td>\n",
       "      <td>0.316834</td>\n",
       "      <td>0.572183</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>742</td>\n",
       "      <td>0.278543</td>\n",
       "      <td>0.552743</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>782</td>\n",
       "      <td>0.337313</td>\n",
       "      <td>0.779202</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>721</td>\n",
       "      <td>0.253597</td>\n",
       "      <td>0.417981</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>697</td>\n",
       "      <td>0.333753</td>\n",
       "      <td>0.580210</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>725</td>\n",
       "      <td>0.284064</td>\n",
       "      <td>0.465774</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>386</td>\n",
       "      <td>0.245261</td>\n",
       "      <td>0.285491</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>716</td>\n",
       "      <td>0.303904</td>\n",
       "      <td>0.424818</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>328</td>\n",
       "      <td>0.332755</td>\n",
       "      <td>0.813538</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>488</td>\n",
       "      <td>0.303440</td>\n",
       "      <td>0.356932</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>738</td>\n",
       "      <td>0.299944</td>\n",
       "      <td>0.704802</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>711</td>\n",
       "      <td>0.364667</td>\n",
       "      <td>0.492997</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>514</td>\n",
       "      <td>0.294900</td>\n",
       "      <td>0.564560</td>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>392</td>\n",
       "      <td>0.316788</td>\n",
       "      <td>0.680224</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>305</td>\n",
       "      <td>0.263030</td>\n",
       "      <td>0.502146</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>260</td>\n",
       "      <td>0.320352</td>\n",
       "      <td>0.859211</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>553</td>\n",
       "      <td>0.303100</td>\n",
       "      <td>0.385399</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>371</td>\n",
       "      <td>0.252450</td>\n",
       "      <td>0.562412</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>28600</td>\n",
       "      <td>0.289313</td>\n",
       "      <td>0.556492</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>472</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.731973</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>457</td>\n",
       "      <td>0.384203</td>\n",
       "      <td>0.750968</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>355</td>\n",
       "      <td>0.270435</td>\n",
       "      <td>0.501408</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>416</td>\n",
       "      <td>0.341266</td>\n",
       "      <td>0.738622</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>17</td>\n",
       "      <td>0.302730</td>\n",
       "      <td>0.355200</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>556</td>\n",
       "      <td>0.325347</td>\n",
       "      <td>0.593496</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>80</td>\n",
       "      <td>0.276110</td>\n",
       "      <td>0.397380</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>14927</td>\n",
       "      <td>0.281573</td>\n",
       "      <td>0.711409</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>741</td>\n",
       "      <td>0.276466</td>\n",
       "      <td>0.345576</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>650</td>\n",
       "      <td>0.267929</td>\n",
       "      <td>0.501916</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>334</td>\n",
       "      <td>0.372246</td>\n",
       "      <td>0.810710</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>688</td>\n",
       "      <td>0.338079</td>\n",
       "      <td>0.681392</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1104</td>\n",
       "      <td>0.295409</td>\n",
       "      <td>0.647287</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>464</td>\n",
       "      <td>0.272608</td>\n",
       "      <td>0.481720</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>505</td>\n",
       "      <td>0.292021</td>\n",
       "      <td>0.538062</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2</td>\n",
       "      <td>0.270286</td>\n",
       "      <td>0.462247</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>368</td>\n",
       "      <td>0.255851</td>\n",
       "      <td>0.409836</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2743</td>\n",
       "      <td>0.259266</td>\n",
       "      <td>0.483301</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team_code  rebounding  win_rate  num_games\n",
       "0          272    0.241530  0.426626        661\n",
       "1          632    0.293107  0.429664        654\n",
       "2          178    0.297945  0.320840        667\n",
       "3          183    0.245612  0.476190        504\n",
       "4           28    0.283190  0.548433        702\n",
       "5          483    0.276217  0.443769        658\n",
       "6            7    0.336807  0.413445        595\n",
       "7          664    0.287098  0.520000        675\n",
       "8          204    0.322037  0.530864        729\n",
       "9          229    0.291606  0.382309        667\n",
       "10         158    0.289716  0.467949        624\n",
       "11         136    0.282258  0.202683        671\n",
       "12         141    0.283392  0.337719        684\n",
       "13         402    0.314090  0.385321        654\n",
       "14         676    0.352438  0.748201        695\n",
       "15         172    0.287586  0.343154        577\n",
       "16        2678    0.282616  0.345312        640\n",
       "17         231    0.295516  0.417630        692\n",
       "18         699    0.315440  0.517974        612\n",
       "19         471    0.287515  0.513514        629\n",
       "20         554    0.255758  0.646865        606\n",
       "21         288    0.331052  0.633001        703\n",
       "22         101    0.297901  0.340989        566\n",
       "23         572    0.316834  0.572183        568\n",
       "24         742    0.278543  0.552743        711\n",
       "25         782    0.337313  0.779202        702\n",
       "26         721    0.253597  0.417981        634\n",
       "27         697    0.333753  0.580210        667\n",
       "28         725    0.284064  0.465774        672\n",
       "29         386    0.245261  0.285491        641\n",
       "..         ...         ...       ...        ...\n",
       "321        716    0.303904  0.424818        685\n",
       "322        328    0.332755  0.813538        783\n",
       "323        488    0.303440  0.356932        678\n",
       "324        738    0.299944  0.704802        708\n",
       "325        711    0.364667  0.492997        714\n",
       "326        514    0.294900  0.564560        728\n",
       "327        392    0.316788  0.680224        713\n",
       "328        305    0.263030  0.502146        699\n",
       "329        260    0.320352  0.859211        760\n",
       "330        553    0.303100  0.385399        589\n",
       "331        371    0.252450  0.562412        713\n",
       "332      28600    0.289313  0.556492        593\n",
       "333        472    0.362197  0.731973        735\n",
       "334        457    0.384203  0.750968        775\n",
       "335        355    0.270435  0.501408        710\n",
       "336        416    0.341266  0.738622        769\n",
       "337         17    0.302730  0.355200        625\n",
       "338        556    0.325347  0.593496        738\n",
       "339         80    0.276110  0.397380        458\n",
       "340      14927    0.281573  0.711409        596\n",
       "341        741    0.276466  0.345576        599\n",
       "342        650    0.267929  0.501916        522\n",
       "343        334    0.372246  0.810710        803\n",
       "344        688    0.338079  0.681392        747\n",
       "345       1104    0.295409  0.647287        516\n",
       "346        464    0.272608  0.481720        465\n",
       "347        505    0.292021  0.538062        578\n",
       "348          2    0.270286  0.462247        543\n",
       "349        368    0.255851  0.409836        488\n",
       "350       2743    0.259266  0.483301        509\n",
       "\n",
       "[351 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery df3\n",
    "SELECT \n",
    "  team_code,\n",
    "  AVG(SAFE_DIVIDE(oreb,oreb + opp_dreb)) AS rebounding,\n",
    "  AVG(win) AS win_rate,\n",
    "  COUNT(win) AS num_games\n",
    "FROM lab_dev.team_box\n",
    "WHERE fga IS NOT NULL\n",
    "GROUP BY team_code\n",
    "HAVING num_games > 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot(x='rebounding', y='win_rate', style='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship doesn't seem all that strong here. One way to measure the strength of the relationship is through the correlation. Numbers near 0 mean not correlated and numbers near +/- 1 indicate high correlation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team_code     0.054005\n",
       "rebounding    0.377354\n",
       "win_rate      1.000000\n",
       "num_games     0.464207\n",
       "Name: win_rate, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.corr()['win_rate']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation between rebounding and win_rate is 0.38.  Compare that to the first data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team_code                        0.054005\n",
       "offensive_shooting_efficiency    0.671579\n",
       "opponents_shooting_efficiency   -0.742178\n",
       "win_rate                         1.000000\n",
       "num_games                        0.464207\n",
       "Name: win_rate, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.corr()['win_rate']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the offensive and opponents efficiency have correlation of 0.67 and -0.66, which are higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team_code           0.054005\n",
       "turnover_percent   -0.551083\n",
       "win_rate            1.000000\n",
       "num_games           0.464207\n",
       "Name: win_rate, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.corr()['win_rate']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Free throw factor\n",
    "\n",
    "This is a measure of both how often a team gets to the line and how often they make them:\n",
    "\n",
    "$FT / FGA$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_code</th>\n",
       "      <th>freethrows</th>\n",
       "      <th>win_rate</th>\n",
       "      <th>num_games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>272</td>\n",
       "      <td>0.243652</td>\n",
       "      <td>0.426626</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>632</td>\n",
       "      <td>0.237715</td>\n",
       "      <td>0.429664</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178</td>\n",
       "      <td>0.222857</td>\n",
       "      <td>0.320840</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>183</td>\n",
       "      <td>0.254770</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>0.288545</td>\n",
       "      <td>0.548433</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>483</td>\n",
       "      <td>0.274948</td>\n",
       "      <td>0.443769</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.248667</td>\n",
       "      <td>0.413445</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>664</td>\n",
       "      <td>0.261282</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>204</td>\n",
       "      <td>0.272575</td>\n",
       "      <td>0.530864</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>229</td>\n",
       "      <td>0.231934</td>\n",
       "      <td>0.382309</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>158</td>\n",
       "      <td>0.248947</td>\n",
       "      <td>0.467949</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>136</td>\n",
       "      <td>0.221863</td>\n",
       "      <td>0.202683</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>141</td>\n",
       "      <td>0.257229</td>\n",
       "      <td>0.337719</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>402</td>\n",
       "      <td>0.253590</td>\n",
       "      <td>0.385321</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>676</td>\n",
       "      <td>0.289181</td>\n",
       "      <td>0.748201</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>172</td>\n",
       "      <td>0.243333</td>\n",
       "      <td>0.343154</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2678</td>\n",
       "      <td>0.252833</td>\n",
       "      <td>0.345312</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>231</td>\n",
       "      <td>0.266086</td>\n",
       "      <td>0.417630</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>699</td>\n",
       "      <td>0.310747</td>\n",
       "      <td>0.517974</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>471</td>\n",
       "      <td>0.264406</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>554</td>\n",
       "      <td>0.267905</td>\n",
       "      <td>0.646865</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>288</td>\n",
       "      <td>0.249748</td>\n",
       "      <td>0.633001</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>101</td>\n",
       "      <td>0.303917</td>\n",
       "      <td>0.340989</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>572</td>\n",
       "      <td>0.272403</td>\n",
       "      <td>0.572183</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>742</td>\n",
       "      <td>0.283348</td>\n",
       "      <td>0.552743</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>782</td>\n",
       "      <td>0.280968</td>\n",
       "      <td>0.779202</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>721</td>\n",
       "      <td>0.249609</td>\n",
       "      <td>0.417981</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>697</td>\n",
       "      <td>0.252731</td>\n",
       "      <td>0.580210</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>725</td>\n",
       "      <td>0.220399</td>\n",
       "      <td>0.465774</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>386</td>\n",
       "      <td>0.283397</td>\n",
       "      <td>0.285491</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>716</td>\n",
       "      <td>0.264069</td>\n",
       "      <td>0.424818</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>328</td>\n",
       "      <td>0.294445</td>\n",
       "      <td>0.813538</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>488</td>\n",
       "      <td>0.288288</td>\n",
       "      <td>0.356932</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>738</td>\n",
       "      <td>0.298986</td>\n",
       "      <td>0.704802</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>711</td>\n",
       "      <td>0.315352</td>\n",
       "      <td>0.492997</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>514</td>\n",
       "      <td>0.301645</td>\n",
       "      <td>0.564560</td>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>392</td>\n",
       "      <td>0.298517</td>\n",
       "      <td>0.680224</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>305</td>\n",
       "      <td>0.294915</td>\n",
       "      <td>0.502146</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>260</td>\n",
       "      <td>0.285509</td>\n",
       "      <td>0.859211</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>553</td>\n",
       "      <td>0.246203</td>\n",
       "      <td>0.385399</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>371</td>\n",
       "      <td>0.257085</td>\n",
       "      <td>0.562412</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>28600</td>\n",
       "      <td>0.302776</td>\n",
       "      <td>0.556492</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>472</td>\n",
       "      <td>0.299636</td>\n",
       "      <td>0.731973</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>457</td>\n",
       "      <td>0.248660</td>\n",
       "      <td>0.750968</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>355</td>\n",
       "      <td>0.269752</td>\n",
       "      <td>0.501408</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>416</td>\n",
       "      <td>0.259399</td>\n",
       "      <td>0.738622</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>17</td>\n",
       "      <td>0.260396</td>\n",
       "      <td>0.355200</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>556</td>\n",
       "      <td>0.303351</td>\n",
       "      <td>0.593496</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>80</td>\n",
       "      <td>0.277944</td>\n",
       "      <td>0.397380</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>14927</td>\n",
       "      <td>0.249898</td>\n",
       "      <td>0.711409</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>741</td>\n",
       "      <td>0.226334</td>\n",
       "      <td>0.345576</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>650</td>\n",
       "      <td>0.270419</td>\n",
       "      <td>0.501916</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>334</td>\n",
       "      <td>0.318962</td>\n",
       "      <td>0.810710</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>688</td>\n",
       "      <td>0.276128</td>\n",
       "      <td>0.681392</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1104</td>\n",
       "      <td>0.306530</td>\n",
       "      <td>0.647287</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>464</td>\n",
       "      <td>0.273508</td>\n",
       "      <td>0.481720</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>505</td>\n",
       "      <td>0.246932</td>\n",
       "      <td>0.538062</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2</td>\n",
       "      <td>0.258238</td>\n",
       "      <td>0.462247</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>368</td>\n",
       "      <td>0.263038</td>\n",
       "      <td>0.409836</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2743</td>\n",
       "      <td>0.325863</td>\n",
       "      <td>0.483301</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team_code  freethrows  win_rate  num_games\n",
       "0          272    0.243652  0.426626        661\n",
       "1          632    0.237715  0.429664        654\n",
       "2          178    0.222857  0.320840        667\n",
       "3          183    0.254770  0.476190        504\n",
       "4           28    0.288545  0.548433        702\n",
       "5          483    0.274948  0.443769        658\n",
       "6            7    0.248667  0.413445        595\n",
       "7          664    0.261282  0.520000        675\n",
       "8          204    0.272575  0.530864        729\n",
       "9          229    0.231934  0.382309        667\n",
       "10         158    0.248947  0.467949        624\n",
       "11         136    0.221863  0.202683        671\n",
       "12         141    0.257229  0.337719        684\n",
       "13         402    0.253590  0.385321        654\n",
       "14         676    0.289181  0.748201        695\n",
       "15         172    0.243333  0.343154        577\n",
       "16        2678    0.252833  0.345312        640\n",
       "17         231    0.266086  0.417630        692\n",
       "18         699    0.310747  0.517974        612\n",
       "19         471    0.264406  0.513514        629\n",
       "20         554    0.267905  0.646865        606\n",
       "21         288    0.249748  0.633001        703\n",
       "22         101    0.303917  0.340989        566\n",
       "23         572    0.272403  0.572183        568\n",
       "24         742    0.283348  0.552743        711\n",
       "25         782    0.280968  0.779202        702\n",
       "26         721    0.249609  0.417981        634\n",
       "27         697    0.252731  0.580210        667\n",
       "28         725    0.220399  0.465774        672\n",
       "29         386    0.283397  0.285491        641\n",
       "..         ...         ...       ...        ...\n",
       "321        716    0.264069  0.424818        685\n",
       "322        328    0.294445  0.813538        783\n",
       "323        488    0.288288  0.356932        678\n",
       "324        738    0.298986  0.704802        708\n",
       "325        711    0.315352  0.492997        714\n",
       "326        514    0.301645  0.564560        728\n",
       "327        392    0.298517  0.680224        713\n",
       "328        305    0.294915  0.502146        699\n",
       "329        260    0.285509  0.859211        760\n",
       "330        553    0.246203  0.385399        589\n",
       "331        371    0.257085  0.562412        713\n",
       "332      28600    0.302776  0.556492        593\n",
       "333        472    0.299636  0.731973        735\n",
       "334        457    0.248660  0.750968        775\n",
       "335        355    0.269752  0.501408        710\n",
       "336        416    0.259399  0.738622        769\n",
       "337         17    0.260396  0.355200        625\n",
       "338        556    0.303351  0.593496        738\n",
       "339         80    0.277944  0.397380        458\n",
       "340      14927    0.249898  0.711409        596\n",
       "341        741    0.226334  0.345576        599\n",
       "342        650    0.270419  0.501916        522\n",
       "343        334    0.318962  0.810710        803\n",
       "344        688    0.276128  0.681392        747\n",
       "345       1104    0.306530  0.647287        516\n",
       "346        464    0.273508  0.481720        465\n",
       "347        505    0.246932  0.538062        578\n",
       "348          2    0.258238  0.462247        543\n",
       "349        368    0.263038  0.409836        488\n",
       "350       2743    0.325863  0.483301        509\n",
       "\n",
       "[351 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery df3\n",
    "SELECT \n",
    "  team_code,\n",
    "  AVG(SAFE_DIVIDE(ftm,fga)) AS freethrows,\n",
    "  AVG(win) AS win_rate,\n",
    "  COUNT(win) AS num_games\n",
    "FROM lab_dev.team_box\n",
    "WHERE fga IS NOT NULL\n",
    "GROUP BY team_code\n",
    "HAVING num_games > 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot(x='freethrows', y='win_rate', style='o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team_code     0.054005\n",
       "freethrows    0.353668\n",
       "win_rate      1.000000\n",
       "num_games     0.464207\n",
       "Name: win_rate, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.corr()['win_rate']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning\n",
    "\n",
    "Let's use these factors to create a simple ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_code</th>\n",
       "      <th>is_home</th>\n",
       "      <th>offensive_shooting_efficiency</th>\n",
       "      <th>opponents_shooting_efficiency</th>\n",
       "      <th>turnover_percent</th>\n",
       "      <th>opponents_turnover_percent</th>\n",
       "      <th>rebounding</th>\n",
       "      <th>opponents_rebounding</th>\n",
       "      <th>freethrows</th>\n",
       "      <th>opponents_freethrows</th>\n",
       "      <th>win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0.396226</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.099875</td>\n",
       "      <td>0.291845</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.188679</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>632</td>\n",
       "      <td>0</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.532258</td>\n",
       "      <td>0.219873</td>\n",
       "      <td>0.358905</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.651515</td>\n",
       "      <td>0.191388</td>\n",
       "      <td>0.290089</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>504980</td>\n",
       "      <td>0</td>\n",
       "      <td>0.463636</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.198777</td>\n",
       "      <td>0.247966</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>0.632353</td>\n",
       "      <td>0.140242</td>\n",
       "      <td>0.275638</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0.526786</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.098928</td>\n",
       "      <td>0.236088</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.196429</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0.608108</td>\n",
       "      <td>0.614286</td>\n",
       "      <td>0.383212</td>\n",
       "      <td>0.332564</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.207502</td>\n",
       "      <td>0.302775</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.204082</td>\n",
       "      <td>0.371429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0.559524</td>\n",
       "      <td>0.542857</td>\n",
       "      <td>0.317726</td>\n",
       "      <td>0.282158</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.201465</td>\n",
       "      <td>0.163339</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.243902</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_code  is_home  offensive_shooting_efficiency  \\\n",
       "0        272        0                       0.396226   \n",
       "1        632        0                       0.360000   \n",
       "2        178        0                       0.520000   \n",
       "3     504980        0                       0.463636   \n",
       "4        183        0                       0.405172   \n",
       "5         28        0                       0.526786   \n",
       "6        183        0                       0.608108   \n",
       "7        183        0                       0.397959   \n",
       "8        183        0                       0.559524   \n",
       "9        183        0                       0.597561   \n",
       "\n",
       "   opponents_shooting_efficiency  turnover_percent  \\\n",
       "0                       0.645161          0.099875   \n",
       "1                       0.532258          0.219873   \n",
       "2                       0.651515          0.191388   \n",
       "3                       0.727273          0.198777   \n",
       "4                       0.632353          0.140242   \n",
       "5                       0.785714          0.098928   \n",
       "6                       0.614286          0.383212   \n",
       "7                       0.600000          0.207502   \n",
       "8                       0.542857          0.317726   \n",
       "9                       0.541667          0.201465   \n",
       "\n",
       "   opponents_turnover_percent  rebounding  opponents_rebounding  freethrows  \\\n",
       "0                    0.291845    0.189189              0.235294    0.188679   \n",
       "1                    0.358905    0.305556              0.434783    0.200000   \n",
       "2                    0.290089    0.185185              0.350000    0.200000   \n",
       "3                    0.247966    0.378378              0.176471    0.254545   \n",
       "4                    0.275638    0.214286              0.227273    0.172414   \n",
       "5                    0.236088    0.266667              0.166667    0.196429   \n",
       "6                    0.332564    0.350000              0.315789    0.189189   \n",
       "7                    0.302775    0.176471              0.210526    0.204082   \n",
       "8                    0.282158    0.208333              0.315789    0.142857   \n",
       "9                    0.163339    0.200000              0.304348    0.243902   \n",
       "\n",
       "   opponents_freethrows  win  \n",
       "0              0.774194    0  \n",
       "1              0.774194    0  \n",
       "2              0.878788    0  \n",
       "3              0.787879    0  \n",
       "4              0.764706    0  \n",
       "5              0.485714    0  \n",
       "6              0.314286    0  \n",
       "7              0.371429    0  \n",
       "8              0.742857    0  \n",
       "9              0.694444    0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "SELECT \n",
    "  team_code,\n",
    "  is_home,\n",
    "  SAFE_DIVIDE(fgm + 0.5 * fgm3,fga) AS offensive_shooting_efficiency,\n",
    "  SAFE_DIVIDE(opp_fgm + 0.5 * opp_fgm3,opp_fga) AS opponents_shooting_efficiency,\n",
    "  SAFE_DIVIDE(tov,fga+0.475*fta+tov-oreb) AS turnover_percent,\n",
    "  SAFE_DIVIDE(opp_tov,opp_fga+0.475*opp_fta+opp_tov-opp_oreb) AS opponents_turnover_percent,\n",
    "  SAFE_DIVIDE(oreb,oreb + opp_dreb) AS rebounding,\n",
    "  SAFE_DIVIDE(opp_oreb,opp_oreb + dreb) AS opponents_rebounding,\n",
    "  SAFE_DIVIDE(ftm,fga) AS freethrows,\n",
    "  SAFE_DIVIDE(opp_ftm,opp_fga) AS opponents_freethrows,\n",
    "  win\n",
    "FROM lab_dev.team_box\n",
    "WHERE fga IS NOT NULL and win IS NOT NULL\n",
    "LIMIT 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "CREATE OR REPLACE MODEL lab_dev.four_factors_model\n",
    "OPTIONS(model_type='logistic_reg', input_label_cols=['win'])\n",
    "AS\n",
    "\n",
    "SELECT \n",
    "  team_code,\n",
    "  is_home,\n",
    "  SAFE_DIVIDE(fgm + 0.5 * fgm3,fga) AS offensive_shooting_efficiency,\n",
    "  SAFE_DIVIDE(opp_fgm + 0.5 * opp_fgm3,opp_fga) AS opponents_shooting_efficiency,\n",
    "  SAFE_DIVIDE(tov,fga+0.475*fta+tov-oreb) AS turnover_percent,\n",
    "  SAFE_DIVIDE(opp_tov,opp_fga+0.475*opp_fta+opp_tov-opp_oreb) AS opponents_turnover_percent,\n",
    "  SAFE_DIVIDE(oreb,oreb + opp_dreb) AS rebounding,\n",
    "  SAFE_DIVIDE(opp_oreb,opp_oreb + dreb) AS opponents_rebounding,\n",
    "  SAFE_DIVIDE(ftm,fga) AS freethrows,\n",
    "  SAFE_DIVIDE(opp_ftm,opp_fga) AS opponents_freethrows,\n",
    "  win\n",
    "FROM lab_dev.team_box\n",
    "WHERE fga IS NOT NULL and win IS NOT NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>log_loss</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.869986</td>\n",
       "      <td>0.872272</td>\n",
       "      <td>0.871723</td>\n",
       "      <td>0.871127</td>\n",
       "      <td>0.326231</td>\n",
       "      <td>0.93667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  accuracy  f1_score  log_loss  roc_auc\n",
       "0   0.869986  0.872272  0.871723  0.871127  0.326231  0.93667"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "SELECT * FROM ML.EVALUATE(MODEL lab_dev.four_factors_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "87% isn't bad, but ... there is a *huge* problem with the above approach.\n",
    "How are we supposed to know Team A's free throw shooting percentage against Team B before the game is played?\n",
    "\n",
    "What we could do is to get the free throw shooting percentage of Team A in the 3 games prior to this one and use that. This requires analytic functions in SQL. If you are not familar with these, make a copy of the select statement and modify it in stages until you grasp what is happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "CREATE OR REPLACE MODEL lab_dev.four_factors_model\n",
    "OPTIONS(model_type='logistic_reg', input_label_cols=['win'])\n",
    "AS\n",
    "\n",
    "WITH all_games AS (\n",
    "SELECT \n",
    "  game_date,\n",
    "  team_code,\n",
    "  is_home,\n",
    "  SAFE_DIVIDE(fgm + 0.5 * fgm3,fga) AS offensive_shooting_efficiency,\n",
    "  SAFE_DIVIDE(opp_fgm + 0.5 * opp_fgm3,opp_fga) AS opponents_shooting_efficiency,\n",
    "  SAFE_DIVIDE(tov,fga+0.475*fta+tov-oreb) AS turnover_percent,\n",
    "  SAFE_DIVIDE(opp_tov,opp_fga+0.475*opp_fta+opp_tov-opp_oreb) AS opponents_turnover_percent,\n",
    "  SAFE_DIVIDE(oreb,oreb + opp_dreb) AS rebounding,\n",
    "  SAFE_DIVIDE(opp_oreb,opp_oreb + dreb) AS opponents_rebounding,\n",
    "  SAFE_DIVIDE(ftm,fga) AS freethrows,\n",
    "  SAFE_DIVIDE(opp_ftm,opp_fga) AS opponents_freethrows,\n",
    "  win\n",
    "FROM lab_dev.team_box\n",
    "WHERE fga IS NOT NULL and win IS NOT NULL\n",
    ")\n",
    "\n",
    ", prevgames AS (\n",
    "SELECT \n",
    "  is_home,\n",
    "  AVG(offensive_shooting_efficiency) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS offensive_shooting_efficiency,\n",
    "  AVG(opponents_shooting_efficiency) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING)AS opponents_shooting_efficiency,\n",
    "  AVG(turnover_percent)\n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS turnover_percent,\n",
    "  AVG(opponents_turnover_percent)\n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS opponents_turnover_percent,\n",
    "  AVG(rebounding)\n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS rebounding,\n",
    "  AVG(opponents_rebounding) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS opponents_rebounding,\n",
    "  AVG(freethrows) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS freethrows,\n",
    "  AVG(opponents_freethrows) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS oppponents_freethrows,\n",
    "  win\n",
    "FROM all_games\n",
    ")\n",
    "\n",
    "SELECT * FROM prevgames\n",
    "WHERE offensive_shooting_efficiency IS NOT NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>log_loss</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.694751</td>\n",
       "      <td>0.697642</td>\n",
       "      <td>0.693924</td>\n",
       "      <td>0.696194</td>\n",
       "      <td>0.578669</td>\n",
       "      <td>0.764541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  accuracy  f1_score  log_loss   roc_auc\n",
       "0   0.694751  0.697642  0.693924  0.696194  0.578669  0.764541"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery \n",
    "SELECT * FROM ML.EVALUATE(MODEL lab_dev.four_factors_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on just the teams' performance coming in, we can predict the outcome of games with a 69.4% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More complex ML model\n",
    "\n",
    "We can write a more complex ML model using Keras and a deep neural network.\n",
    "The code is not that hard but you'll have to do a lot more work (scaling, hyperparameter tuning)\n",
    "to get better performance than you did with the BigQuery ML model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_home</th>\n",
       "      <th>offensive_shooting_efficiency</th>\n",
       "      <th>opponents_shooting_efficiency</th>\n",
       "      <th>turnover_percent</th>\n",
       "      <th>opponents_turnover_percent</th>\n",
       "      <th>rebounding</th>\n",
       "      <th>opponents_rebounding</th>\n",
       "      <th>freethrows</th>\n",
       "      <th>oppponents_freethrows</th>\n",
       "      <th>win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.404762</td>\n",
       "      <td>0.577586</td>\n",
       "      <td>0.129534</td>\n",
       "      <td>0.132319</td>\n",
       "      <td>0.255814</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.465517</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.564655</td>\n",
       "      <td>0.117888</td>\n",
       "      <td>0.135268</td>\n",
       "      <td>0.221657</td>\n",
       "      <td>0.267361</td>\n",
       "      <td>0.390476</td>\n",
       "      <td>0.370690</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.409668</td>\n",
       "      <td>0.577586</td>\n",
       "      <td>0.129161</td>\n",
       "      <td>0.132480</td>\n",
       "      <td>0.246537</td>\n",
       "      <td>0.321098</td>\n",
       "      <td>0.381530</td>\n",
       "      <td>0.465517</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.407251</td>\n",
       "      <td>0.549261</td>\n",
       "      <td>0.172692</td>\n",
       "      <td>0.135533</td>\n",
       "      <td>0.268236</td>\n",
       "      <td>0.369395</td>\n",
       "      <td>0.379897</td>\n",
       "      <td>0.451817</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.396338</td>\n",
       "      <td>0.529865</td>\n",
       "      <td>0.198496</td>\n",
       "      <td>0.102454</td>\n",
       "      <td>0.245949</td>\n",
       "      <td>0.396533</td>\n",
       "      <td>0.409659</td>\n",
       "      <td>0.403619</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.362626</td>\n",
       "      <td>0.495194</td>\n",
       "      <td>0.265873</td>\n",
       "      <td>0.139328</td>\n",
       "      <td>0.332407</td>\n",
       "      <td>0.497227</td>\n",
       "      <td>0.377841</td>\n",
       "      <td>0.486827</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0.371149</td>\n",
       "      <td>0.473499</td>\n",
       "      <td>0.311279</td>\n",
       "      <td>0.147634</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.508506</td>\n",
       "      <td>0.286932</td>\n",
       "      <td>0.381368</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0.396149</td>\n",
       "      <td>0.493297</td>\n",
       "      <td>0.275781</td>\n",
       "      <td>0.202785</td>\n",
       "      <td>0.263889</td>\n",
       "      <td>0.484101</td>\n",
       "      <td>0.193182</td>\n",
       "      <td>0.322167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0.407723</td>\n",
       "      <td>0.500373</td>\n",
       "      <td>0.280093</td>\n",
       "      <td>0.269113</td>\n",
       "      <td>0.267677</td>\n",
       "      <td>0.491741</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.305872</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0.376692</td>\n",
       "      <td>0.547112</td>\n",
       "      <td>0.243627</td>\n",
       "      <td>0.218389</td>\n",
       "      <td>0.171843</td>\n",
       "      <td>0.371205</td>\n",
       "      <td>0.057692</td>\n",
       "      <td>0.237032</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0.370739</td>\n",
       "      <td>0.615526</td>\n",
       "      <td>0.241415</td>\n",
       "      <td>0.192551</td>\n",
       "      <td>0.221843</td>\n",
       "      <td>0.320966</td>\n",
       "      <td>0.093407</td>\n",
       "      <td>0.243215</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0.317498</td>\n",
       "      <td>0.653836</td>\n",
       "      <td>0.270233</td>\n",
       "      <td>0.118722</td>\n",
       "      <td>0.227263</td>\n",
       "      <td>0.266799</td>\n",
       "      <td>0.139703</td>\n",
       "      <td>0.273507</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.321416</td>\n",
       "      <td>0.657631</td>\n",
       "      <td>0.253746</td>\n",
       "      <td>0.068291</td>\n",
       "      <td>0.273914</td>\n",
       "      <td>0.220563</td>\n",
       "      <td>0.264703</td>\n",
       "      <td>0.373794</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0.361993</td>\n",
       "      <td>0.630026</td>\n",
       "      <td>0.247155</td>\n",
       "      <td>0.071182</td>\n",
       "      <td>0.312803</td>\n",
       "      <td>0.243672</td>\n",
       "      <td>0.292011</td>\n",
       "      <td>0.415461</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0.425237</td>\n",
       "      <td>0.542446</td>\n",
       "      <td>0.221989</td>\n",
       "      <td>0.088116</td>\n",
       "      <td>0.288293</td>\n",
       "      <td>0.228122</td>\n",
       "      <td>0.297963</td>\n",
       "      <td>0.450944</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0.466282</td>\n",
       "      <td>0.462653</td>\n",
       "      <td>0.236614</td>\n",
       "      <td>0.124585</td>\n",
       "      <td>0.361246</td>\n",
       "      <td>0.229592</td>\n",
       "      <td>0.404106</td>\n",
       "      <td>0.412888</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0.476893</td>\n",
       "      <td>0.421784</td>\n",
       "      <td>0.258220</td>\n",
       "      <td>0.178508</td>\n",
       "      <td>0.381641</td>\n",
       "      <td>0.246259</td>\n",
       "      <td>0.330830</td>\n",
       "      <td>0.310714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0.495422</td>\n",
       "      <td>0.409388</td>\n",
       "      <td>0.269521</td>\n",
       "      <td>0.232157</td>\n",
       "      <td>0.384199</td>\n",
       "      <td>0.237436</td>\n",
       "      <td>0.304653</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0.492198</td>\n",
       "      <td>0.401888</td>\n",
       "      <td>0.278082</td>\n",
       "      <td>0.275053</td>\n",
       "      <td>0.421530</td>\n",
       "      <td>0.251877</td>\n",
       "      <td>0.318542</td>\n",
       "      <td>0.260714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0.509110</td>\n",
       "      <td>0.465395</td>\n",
       "      <td>0.234967</td>\n",
       "      <td>0.251346</td>\n",
       "      <td>0.311039</td>\n",
       "      <td>0.318054</td>\n",
       "      <td>0.213273</td>\n",
       "      <td>0.317105</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0.522538</td>\n",
       "      <td>0.516946</td>\n",
       "      <td>0.199711</td>\n",
       "      <td>0.205544</td>\n",
       "      <td>0.215205</td>\n",
       "      <td>0.328470</td>\n",
       "      <td>0.200010</td>\n",
       "      <td>0.310209</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0.516138</td>\n",
       "      <td>0.576589</td>\n",
       "      <td>0.185148</td>\n",
       "      <td>0.164143</td>\n",
       "      <td>0.165670</td>\n",
       "      <td>0.417359</td>\n",
       "      <td>0.196742</td>\n",
       "      <td>0.353066</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0.498529</td>\n",
       "      <td>0.627498</td>\n",
       "      <td>0.154878</td>\n",
       "      <td>0.132432</td>\n",
       "      <td>0.169516</td>\n",
       "      <td>0.425286</td>\n",
       "      <td>0.234937</td>\n",
       "      <td>0.493975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0.499494</td>\n",
       "      <td>0.573770</td>\n",
       "      <td>0.158281</td>\n",
       "      <td>0.150213</td>\n",
       "      <td>0.163936</td>\n",
       "      <td>0.374306</td>\n",
       "      <td>0.233222</td>\n",
       "      <td>0.516453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0.487742</td>\n",
       "      <td>0.554936</td>\n",
       "      <td>0.164454</td>\n",
       "      <td>0.171188</td>\n",
       "      <td>0.217782</td>\n",
       "      <td>0.350556</td>\n",
       "      <td>0.268834</td>\n",
       "      <td>0.636393</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0.445234</td>\n",
       "      <td>0.572793</td>\n",
       "      <td>0.154771</td>\n",
       "      <td>0.186478</td>\n",
       "      <td>0.256552</td>\n",
       "      <td>0.261667</td>\n",
       "      <td>0.228430</td>\n",
       "      <td>0.597703</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0.426768</td>\n",
       "      <td>0.550336</td>\n",
       "      <td>0.140307</td>\n",
       "      <td>0.210642</td>\n",
       "      <td>0.203427</td>\n",
       "      <td>0.300303</td>\n",
       "      <td>0.202862</td>\n",
       "      <td>0.453222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0.384722</td>\n",
       "      <td>0.607281</td>\n",
       "      <td>0.126310</td>\n",
       "      <td>0.220407</td>\n",
       "      <td>0.244780</td>\n",
       "      <td>0.316970</td>\n",
       "      <td>0.193771</td>\n",
       "      <td>0.427528</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.406818</td>\n",
       "      <td>0.586706</td>\n",
       "      <td>0.126736</td>\n",
       "      <td>0.238385</td>\n",
       "      <td>0.299267</td>\n",
       "      <td>0.299601</td>\n",
       "      <td>0.195455</td>\n",
       "      <td>0.344841</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0.466436</td>\n",
       "      <td>0.484733</td>\n",
       "      <td>0.130789</td>\n",
       "      <td>0.257743</td>\n",
       "      <td>0.339194</td>\n",
       "      <td>0.309125</td>\n",
       "      <td>0.223781</td>\n",
       "      <td>0.302078</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242570</th>\n",
       "      <td>0</td>\n",
       "      <td>0.494877</td>\n",
       "      <td>0.636939</td>\n",
       "      <td>0.250341</td>\n",
       "      <td>0.106176</td>\n",
       "      <td>0.212605</td>\n",
       "      <td>0.447520</td>\n",
       "      <td>0.063715</td>\n",
       "      <td>0.154970</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242571</th>\n",
       "      <td>0</td>\n",
       "      <td>0.401742</td>\n",
       "      <td>0.596582</td>\n",
       "      <td>0.222527</td>\n",
       "      <td>0.114765</td>\n",
       "      <td>0.187473</td>\n",
       "      <td>0.438682</td>\n",
       "      <td>0.118210</td>\n",
       "      <td>0.144701</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242572</th>\n",
       "      <td>0</td>\n",
       "      <td>0.406288</td>\n",
       "      <td>0.565718</td>\n",
       "      <td>0.183735</td>\n",
       "      <td>0.121739</td>\n",
       "      <td>0.156888</td>\n",
       "      <td>0.462093</td>\n",
       "      <td>0.135634</td>\n",
       "      <td>0.160133</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242573</th>\n",
       "      <td>0</td>\n",
       "      <td>0.410285</td>\n",
       "      <td>0.573655</td>\n",
       "      <td>0.187710</td>\n",
       "      <td>0.151244</td>\n",
       "      <td>0.180989</td>\n",
       "      <td>0.465588</td>\n",
       "      <td>0.180486</td>\n",
       "      <td>0.199094</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242574</th>\n",
       "      <td>0</td>\n",
       "      <td>0.360669</td>\n",
       "      <td>0.601036</td>\n",
       "      <td>0.267922</td>\n",
       "      <td>0.165863</td>\n",
       "      <td>0.247436</td>\n",
       "      <td>0.426325</td>\n",
       "      <td>0.222152</td>\n",
       "      <td>0.197406</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242575</th>\n",
       "      <td>0</td>\n",
       "      <td>0.462424</td>\n",
       "      <td>0.642155</td>\n",
       "      <td>0.274116</td>\n",
       "      <td>0.190360</td>\n",
       "      <td>0.304081</td>\n",
       "      <td>0.441699</td>\n",
       "      <td>0.228002</td>\n",
       "      <td>0.257028</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242576</th>\n",
       "      <td>0</td>\n",
       "      <td>0.459701</td>\n",
       "      <td>0.713249</td>\n",
       "      <td>0.281827</td>\n",
       "      <td>0.231361</td>\n",
       "      <td>0.283530</td>\n",
       "      <td>0.433245</td>\n",
       "      <td>0.217349</td>\n",
       "      <td>0.248300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242577</th>\n",
       "      <td>0</td>\n",
       "      <td>0.423924</td>\n",
       "      <td>0.731165</td>\n",
       "      <td>0.263945</td>\n",
       "      <td>0.227899</td>\n",
       "      <td>0.217062</td>\n",
       "      <td>0.421066</td>\n",
       "      <td>0.172498</td>\n",
       "      <td>0.199260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242578</th>\n",
       "      <td>0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.284775</td>\n",
       "      <td>0.131291</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.321429</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242579</th>\n",
       "      <td>0</td>\n",
       "      <td>0.318913</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.241916</td>\n",
       "      <td>0.128292</td>\n",
       "      <td>0.089744</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.259306</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242580</th>\n",
       "      <td>0</td>\n",
       "      <td>0.306250</td>\n",
       "      <td>0.542683</td>\n",
       "      <td>0.277932</td>\n",
       "      <td>0.173348</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.395833</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242581</th>\n",
       "      <td>0</td>\n",
       "      <td>0.300561</td>\n",
       "      <td>0.551829</td>\n",
       "      <td>0.281994</td>\n",
       "      <td>0.142076</td>\n",
       "      <td>0.308007</td>\n",
       "      <td>0.371830</td>\n",
       "      <td>0.038141</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242582</th>\n",
       "      <td>0</td>\n",
       "      <td>0.306065</td>\n",
       "      <td>0.542683</td>\n",
       "      <td>0.278273</td>\n",
       "      <td>0.172442</td>\n",
       "      <td>0.272005</td>\n",
       "      <td>0.394553</td>\n",
       "      <td>0.025427</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242583</th>\n",
       "      <td>0</td>\n",
       "      <td>0.324143</td>\n",
       "      <td>0.556318</td>\n",
       "      <td>0.272739</td>\n",
       "      <td>0.181963</td>\n",
       "      <td>0.252080</td>\n",
       "      <td>0.386824</td>\n",
       "      <td>0.039341</td>\n",
       "      <td>0.250169</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242584</th>\n",
       "      <td>0</td>\n",
       "      <td>0.326806</td>\n",
       "      <td>0.565868</td>\n",
       "      <td>0.263222</td>\n",
       "      <td>0.190830</td>\n",
       "      <td>0.244469</td>\n",
       "      <td>0.379329</td>\n",
       "      <td>0.075344</td>\n",
       "      <td>0.258419</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242585</th>\n",
       "      <td>0</td>\n",
       "      <td>0.315588</td>\n",
       "      <td>0.566249</td>\n",
       "      <td>0.246991</td>\n",
       "      <td>0.214809</td>\n",
       "      <td>0.229691</td>\n",
       "      <td>0.384478</td>\n",
       "      <td>0.128700</td>\n",
       "      <td>0.246033</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242586</th>\n",
       "      <td>0</td>\n",
       "      <td>0.316448</td>\n",
       "      <td>0.610151</td>\n",
       "      <td>0.229092</td>\n",
       "      <td>0.191450</td>\n",
       "      <td>0.237726</td>\n",
       "      <td>0.338994</td>\n",
       "      <td>0.144726</td>\n",
       "      <td>0.254396</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242587</th>\n",
       "      <td>0</td>\n",
       "      <td>0.292800</td>\n",
       "      <td>0.623346</td>\n",
       "      <td>0.227558</td>\n",
       "      <td>0.182843</td>\n",
       "      <td>0.252149</td>\n",
       "      <td>0.283799</td>\n",
       "      <td>0.124456</td>\n",
       "      <td>0.244673</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242588</th>\n",
       "      <td>0</td>\n",
       "      <td>0.353846</td>\n",
       "      <td>0.657534</td>\n",
       "      <td>0.142626</td>\n",
       "      <td>0.163419</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.107692</td>\n",
       "      <td>0.109589</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242589</th>\n",
       "      <td>0</td>\n",
       "      <td>0.392440</td>\n",
       "      <td>0.639578</td>\n",
       "      <td>0.099742</td>\n",
       "      <td>0.135365</td>\n",
       "      <td>0.110119</td>\n",
       "      <td>0.369318</td>\n",
       "      <td>0.174536</td>\n",
       "      <td>0.081822</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242590</th>\n",
       "      <td>0</td>\n",
       "      <td>0.358849</td>\n",
       "      <td>0.657867</td>\n",
       "      <td>0.138008</td>\n",
       "      <td>0.161207</td>\n",
       "      <td>0.122795</td>\n",
       "      <td>0.375842</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.110103</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242591</th>\n",
       "      <td>0</td>\n",
       "      <td>0.355675</td>\n",
       "      <td>0.653556</td>\n",
       "      <td>0.159511</td>\n",
       "      <td>0.163903</td>\n",
       "      <td>0.103460</td>\n",
       "      <td>0.353310</td>\n",
       "      <td>0.173806</td>\n",
       "      <td>0.176327</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242592</th>\n",
       "      <td>0</td>\n",
       "      <td>0.381499</td>\n",
       "      <td>0.633404</td>\n",
       "      <td>0.188832</td>\n",
       "      <td>0.161908</td>\n",
       "      <td>0.151756</td>\n",
       "      <td>0.426227</td>\n",
       "      <td>0.146883</td>\n",
       "      <td>0.206622</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242593</th>\n",
       "      <td>0</td>\n",
       "      <td>0.404762</td>\n",
       "      <td>0.471831</td>\n",
       "      <td>0.311025</td>\n",
       "      <td>0.247678</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242594</th>\n",
       "      <td>0</td>\n",
       "      <td>0.422078</td>\n",
       "      <td>0.465645</td>\n",
       "      <td>0.282698</td>\n",
       "      <td>0.275601</td>\n",
       "      <td>0.196970</td>\n",
       "      <td>0.424783</td>\n",
       "      <td>0.216450</td>\n",
       "      <td>0.296536</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242595</th>\n",
       "      <td>0</td>\n",
       "      <td>0.372340</td>\n",
       "      <td>0.475806</td>\n",
       "      <td>0.302001</td>\n",
       "      <td>0.200215</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.170213</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242596</th>\n",
       "      <td>0</td>\n",
       "      <td>0.356625</td>\n",
       "      <td>0.429080</td>\n",
       "      <td>0.323144</td>\n",
       "      <td>0.186438</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.457002</td>\n",
       "      <td>0.085106</td>\n",
       "      <td>0.246679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242597</th>\n",
       "      <td>0</td>\n",
       "      <td>0.371083</td>\n",
       "      <td>0.482482</td>\n",
       "      <td>0.302957</td>\n",
       "      <td>0.200104</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.460224</td>\n",
       "      <td>0.163404</td>\n",
       "      <td>0.259691</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242598</th>\n",
       "      <td>0</td>\n",
       "      <td>0.367255</td>\n",
       "      <td>0.518111</td>\n",
       "      <td>0.286675</td>\n",
       "      <td>0.187142</td>\n",
       "      <td>0.165248</td>\n",
       "      <td>0.446730</td>\n",
       "      <td>0.136976</td>\n",
       "      <td>0.253102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242599</th>\n",
       "      <td>0</td>\n",
       "      <td>0.362133</td>\n",
       "      <td>0.554332</td>\n",
       "      <td>0.263531</td>\n",
       "      <td>0.172676</td>\n",
       "      <td>0.146130</td>\n",
       "      <td>0.439008</td>\n",
       "      <td>0.103682</td>\n",
       "      <td>0.283413</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242600 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        is_home  offensive_shooting_efficiency  opponents_shooting_efficiency  \\\n",
       "0             0                       0.404762                       0.577586   \n",
       "1             0                       0.485714                       0.564655   \n",
       "2             0                       0.409668                       0.577586   \n",
       "3             0                       0.407251                       0.549261   \n",
       "4             0                       0.396338                       0.529865   \n",
       "5             0                       0.362626                       0.495194   \n",
       "6             0                       0.371149                       0.473499   \n",
       "7             0                       0.396149                       0.493297   \n",
       "8             0                       0.407723                       0.500373   \n",
       "9             0                       0.376692                       0.547112   \n",
       "10            0                       0.370739                       0.615526   \n",
       "11            0                       0.317498                       0.653836   \n",
       "12            0                       0.321416                       0.657631   \n",
       "13            0                       0.361993                       0.630026   \n",
       "14            0                       0.425237                       0.542446   \n",
       "15            0                       0.466282                       0.462653   \n",
       "16            0                       0.476893                       0.421784   \n",
       "17            0                       0.495422                       0.409388   \n",
       "18            0                       0.492198                       0.401888   \n",
       "19            0                       0.509110                       0.465395   \n",
       "20            0                       0.522538                       0.516946   \n",
       "21            1                       0.516138                       0.576589   \n",
       "22            1                       0.498529                       0.627498   \n",
       "23            1                       0.499494                       0.573770   \n",
       "24            0                       0.487742                       0.554936   \n",
       "25            0                       0.445234                       0.572793   \n",
       "26            0                       0.426768                       0.550336   \n",
       "27            1                       0.384722                       0.607281   \n",
       "28            1                       0.406818                       0.586706   \n",
       "29            1                       0.466436                       0.484733   \n",
       "...         ...                            ...                            ...   \n",
       "242570        0                       0.494877                       0.636939   \n",
       "242571        0                       0.401742                       0.596582   \n",
       "242572        0                       0.406288                       0.565718   \n",
       "242573        0                       0.410285                       0.573655   \n",
       "242574        0                       0.360669                       0.601036   \n",
       "242575        0                       0.462424                       0.642155   \n",
       "242576        0                       0.459701                       0.713249   \n",
       "242577        0                       0.423924                       0.731165   \n",
       "242578        0                       0.285714                       0.600000   \n",
       "242579        0                       0.318913                       0.583333   \n",
       "242580        0                       0.306250                       0.542683   \n",
       "242581        0                       0.300561                       0.551829   \n",
       "242582        0                       0.306065                       0.542683   \n",
       "242583        0                       0.324143                       0.556318   \n",
       "242584        0                       0.326806                       0.565868   \n",
       "242585        0                       0.315588                       0.566249   \n",
       "242586        0                       0.316448                       0.610151   \n",
       "242587        0                       0.292800                       0.623346   \n",
       "242588        0                       0.353846                       0.657534   \n",
       "242589        0                       0.392440                       0.639578   \n",
       "242590        0                       0.358849                       0.657867   \n",
       "242591        0                       0.355675                       0.653556   \n",
       "242592        0                       0.381499                       0.633404   \n",
       "242593        0                       0.404762                       0.471831   \n",
       "242594        0                       0.422078                       0.465645   \n",
       "242595        0                       0.372340                       0.475806   \n",
       "242596        0                       0.356625                       0.429080   \n",
       "242597        0                       0.371083                       0.482482   \n",
       "242598        0                       0.367255                       0.518111   \n",
       "242599        0                       0.362133                       0.554332   \n",
       "\n",
       "        turnover_percent  opponents_turnover_percent  rebounding  \\\n",
       "0               0.129534                    0.132319    0.255814   \n",
       "1               0.117888                    0.135268    0.221657   \n",
       "2               0.129161                    0.132480    0.246537   \n",
       "3               0.172692                    0.135533    0.268236   \n",
       "4               0.198496                    0.102454    0.245949   \n",
       "5               0.265873                    0.139328    0.332407   \n",
       "6               0.311279                    0.147634    0.291667   \n",
       "7               0.275781                    0.202785    0.263889   \n",
       "8               0.280093                    0.269113    0.267677   \n",
       "9               0.243627                    0.218389    0.171843   \n",
       "10              0.241415                    0.192551    0.221843   \n",
       "11              0.270233                    0.118722    0.227263   \n",
       "12              0.253746                    0.068291    0.273914   \n",
       "13              0.247155                    0.071182    0.312803   \n",
       "14              0.221989                    0.088116    0.288293   \n",
       "15              0.236614                    0.124585    0.361246   \n",
       "16              0.258220                    0.178508    0.381641   \n",
       "17              0.269521                    0.232157    0.384199   \n",
       "18              0.278082                    0.275053    0.421530   \n",
       "19              0.234967                    0.251346    0.311039   \n",
       "20              0.199711                    0.205544    0.215205   \n",
       "21              0.185148                    0.164143    0.165670   \n",
       "22              0.154878                    0.132432    0.169516   \n",
       "23              0.158281                    0.150213    0.163936   \n",
       "24              0.164454                    0.171188    0.217782   \n",
       "25              0.154771                    0.186478    0.256552   \n",
       "26              0.140307                    0.210642    0.203427   \n",
       "27              0.126310                    0.220407    0.244780   \n",
       "28              0.126736                    0.238385    0.299267   \n",
       "29              0.130789                    0.257743    0.339194   \n",
       "...                  ...                         ...         ...   \n",
       "242570          0.250341                    0.106176    0.212605   \n",
       "242571          0.222527                    0.114765    0.187473   \n",
       "242572          0.183735                    0.121739    0.156888   \n",
       "242573          0.187710                    0.151244    0.180989   \n",
       "242574          0.267922                    0.165863    0.247436   \n",
       "242575          0.274116                    0.190360    0.304081   \n",
       "242576          0.281827                    0.231361    0.283530   \n",
       "242577          0.263945                    0.227899    0.217062   \n",
       "242578          0.284775                    0.131291    0.083333   \n",
       "242579          0.241916                    0.128292    0.089744   \n",
       "242580          0.277932                    0.173348    0.271186   \n",
       "242581          0.281994                    0.142076    0.308007   \n",
       "242582          0.278273                    0.172442    0.272005   \n",
       "242583          0.272739                    0.181963    0.252080   \n",
       "242584          0.263222                    0.190830    0.244469   \n",
       "242585          0.246991                    0.214809    0.229691   \n",
       "242586          0.229092                    0.191450    0.237726   \n",
       "242587          0.227558                    0.182843    0.252149   \n",
       "242588          0.142626                    0.163419    0.125000   \n",
       "242589          0.099742                    0.135365    0.110119   \n",
       "242590          0.138008                    0.161207    0.122795   \n",
       "242591          0.159511                    0.163903    0.103460   \n",
       "242592          0.188832                    0.161908    0.151756   \n",
       "242593          0.311025                    0.247678    0.227273   \n",
       "242594          0.282698                    0.275601    0.196970   \n",
       "242595          0.302001                    0.200215    0.176471   \n",
       "242596          0.323144                    0.186438    0.147059   \n",
       "242597          0.302957                    0.200104    0.176471   \n",
       "242598          0.286675                    0.187142    0.165248   \n",
       "242599          0.263531                    0.172676    0.146130   \n",
       "\n",
       "        opponents_rebounding  freethrows  oppponents_freethrows  win  \n",
       "0                   0.312500    0.380952               0.465517    0  \n",
       "1                   0.267361    0.390476               0.370690    0  \n",
       "2                   0.321098    0.381530               0.465517    0  \n",
       "3                   0.369395    0.379897               0.451817    0  \n",
       "4                   0.396533    0.409659               0.403619    0  \n",
       "5                   0.497227    0.377841               0.486827    0  \n",
       "6                   0.508506    0.286932               0.381368    0  \n",
       "7                   0.484101    0.193182               0.322167    0  \n",
       "8                   0.491741    0.068182               0.305872    0  \n",
       "9                   0.371205    0.057692               0.237032    0  \n",
       "10                  0.320966    0.093407               0.243215    0  \n",
       "11                  0.266799    0.139703               0.273507    0  \n",
       "12                  0.220563    0.264703               0.373794    0  \n",
       "13                  0.243672    0.292011               0.415461    0  \n",
       "14                  0.228122    0.297963               0.450944    1  \n",
       "15                  0.229592    0.404106               0.412888    1  \n",
       "16                  0.246259    0.330830               0.310714    1  \n",
       "17                  0.237436    0.304653               0.285714    1  \n",
       "18                  0.251877    0.318542               0.260714    0  \n",
       "19                  0.318054    0.213273               0.317105    0  \n",
       "20                  0.328470    0.200010               0.310209    0  \n",
       "21                  0.417359    0.196742               0.353066    0  \n",
       "22                  0.425286    0.234937               0.493975    0  \n",
       "23                  0.374306    0.233222               0.516453    0  \n",
       "24                  0.350556    0.268834               0.636393    0  \n",
       "25                  0.261667    0.228430               0.597703    0  \n",
       "26                  0.300303    0.202862               0.453222    0  \n",
       "27                  0.316970    0.193771               0.427528    1  \n",
       "28                  0.299601    0.195455               0.344841    1  \n",
       "29                  0.309125    0.223781               0.302078    1  \n",
       "...                      ...         ...                    ...  ...  \n",
       "242570              0.447520    0.063715               0.154970    0  \n",
       "242571              0.438682    0.118210               0.144701    0  \n",
       "242572              0.462093    0.135634               0.160133    0  \n",
       "242573              0.465588    0.180486               0.199094    0  \n",
       "242574              0.426325    0.222152               0.197406    0  \n",
       "242575              0.441699    0.228002               0.257028    0  \n",
       "242576              0.433245    0.217349               0.248300    0  \n",
       "242577              0.421066    0.172498               0.199260    0  \n",
       "242578              0.458333    0.321429               0.177778    0  \n",
       "242579              0.437500    0.259306               0.216667    0  \n",
       "242580              0.395833    0.025000               0.231707    0  \n",
       "242581              0.371830    0.038141               0.250000    0  \n",
       "242582              0.394553    0.025427               0.231707    0  \n",
       "242583              0.386824    0.039341               0.250169    0  \n",
       "242584              0.379329    0.075344               0.258419    0  \n",
       "242585              0.384478    0.128700               0.246033    0  \n",
       "242586              0.338994    0.144726               0.254396    0  \n",
       "242587              0.283799    0.124456               0.244673    0  \n",
       "242588              0.375000    0.107692               0.109589    0  \n",
       "242589              0.369318    0.174536               0.081822    0  \n",
       "242590              0.375842    0.116357               0.110103    0  \n",
       "242591              0.353310    0.173806               0.176327    0  \n",
       "242592              0.426227    0.146883               0.206622    0  \n",
       "242593              0.369565    0.190476               0.295775    0  \n",
       "242594              0.424783    0.216450               0.296536    0  \n",
       "242595              0.459459    0.170213               0.258065    0  \n",
       "242596              0.457002    0.085106               0.246679    0  \n",
       "242597              0.460224    0.163404               0.259691    0  \n",
       "242598              0.446730    0.136976               0.253102    0  \n",
       "242599              0.439008    0.103682               0.283413    0  \n",
       "\n",
       "[242600 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery games\n",
    "WITH all_games AS (\n",
    "SELECT \n",
    "  game_date,\n",
    "  team_code,\n",
    "  is_home,\n",
    "  SAFE_DIVIDE(fgm + 0.5 * fgm3,fga) AS offensive_shooting_efficiency,\n",
    "  SAFE_DIVIDE(opp_fgm + 0.5 * opp_fgm3,opp_fga) AS opponents_shooting_efficiency,\n",
    "  SAFE_DIVIDE(tov,fga+0.475*fta+tov-oreb) AS turnover_percent,\n",
    "  SAFE_DIVIDE(opp_tov,opp_fga+0.475*opp_fta+opp_tov-opp_oreb) AS opponents_turnover_percent,\n",
    "  SAFE_DIVIDE(oreb,oreb + opp_dreb) AS rebounding,\n",
    "  SAFE_DIVIDE(opp_oreb,opp_oreb + dreb) AS opponents_rebounding,\n",
    "  SAFE_DIVIDE(ftm,fga) AS freethrows,\n",
    "  SAFE_DIVIDE(opp_ftm,opp_fga) AS opponents_freethrows,\n",
    "  win\n",
    "FROM lab_dev.team_box\n",
    "WHERE fga IS NOT NULL and win IS NOT NULL\n",
    ")\n",
    "\n",
    ", prevgames AS (\n",
    "SELECT \n",
    "  is_home,\n",
    "  AVG(offensive_shooting_efficiency) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS offensive_shooting_efficiency,\n",
    "  AVG(opponents_shooting_efficiency) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING)AS opponents_shooting_efficiency,\n",
    "  AVG(turnover_percent)\n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS turnover_percent,\n",
    "  AVG(opponents_turnover_percent)\n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS opponents_turnover_percent,\n",
    "  AVG(rebounding)\n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS rebounding,\n",
    "  AVG(opponents_rebounding) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS opponents_rebounding,\n",
    "  AVG(freethrows) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS freethrows,\n",
    "  AVG(opponents_freethrows) \n",
    "       OVER(PARTITION BY team_code ORDER BY game_date ASC ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING) AS oppponents_freethrows,\n",
    "  win\n",
    "FROM all_games\n",
    ")\n",
    "\n",
    "SELECT * FROM prevgames\n",
    "WHERE offensive_shooting_efficiency IS NOT NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "242600 10 169820\n"
     ]
    }
   ],
   "source": [
    "nrows = len(games)\n",
    "ncols = len(games.iloc[0])\n",
    "ntrain = (nrows * 7) // 10\n",
    "print(nrows, ncols, ntrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0:ntrain are the training data; remaining rows are testing\n",
    "# last col is the label\n",
    "train_x = games.iloc[:ntrain, 0:(ncols-1)]\n",
    "train_y = games.iloc[:ntrain, ncols-1]\n",
    "test_x = games.iloc[ntrain:, 0:(ncols-1)]\n",
    "test_y = games.iloc[ntrain:, ncols-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Dense(5, input_dim=ncols-1, activation='relu'))\n",
    "model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/5\n",
      "169820/169820 [==============================] - 11s 65us/sample - loss: 0.6281 - acc: 0.6539\n",
      "Epoch 2/5\n",
      "169820/169820 [==============================] - 9s 53us/sample - loss: 0.5976 - acc: 0.6771\n",
      "Epoch 3/5\n",
      "169820/169820 [==============================] - 10s 56us/sample - loss: 0.5907 - acc: 0.6841\n",
      "Epoch 4/5\n",
      "169820/169820 [==============================] - 10s 58us/sample - loss: 0.5886 - acc: 0.6861\n",
      "Epoch 5/5\n",
      "169820/169820 [==============================] - 9s 52us/sample - loss: 0.5877 - acc: 0.6860\n",
      "72780/72780 [==============================] - 0s 4us/sample - loss: 0.5494 - acc: 0.7151\n",
      "[0.5493521325746779, 0.71512777]\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_x, train_y, epochs=5, batch_size=32)\n",
    "score = model.evaluate(test_x, test_y, batch_size=512)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a deep neural network, we are able to get 71.5% accuracy using the four factors model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2019 Google Inc. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  }
 ],
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